Showcasing the TAIAO project: providing resources for machine learning from images of New Zealand's natural environment.

IF 2.1 4区 综合性期刊 Q2 MULTIDISCIPLINARY SCIENCES Journal of the Royal Society of New Zealand Pub Date : 2022-09-19 eCollection Date: 2023-01-01 DOI:10.1080/03036758.2022.2118321
Nick Lim, Albert Bifet, Daniel Bull, Eibe Frank, Yunzhe Jia, Jacob Montiel, Bernhard Pfahringer
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Abstract

Proper management of the earth's natural resources is imperative to combat further degradation of the natural environment. However, the environmental datasets necessary for informed resource planning and conservation can be costly to collect and annotate. Consequently, there is a lack of publicly available datasets, particularly annotated image datasets relevant for environmental conservation, that can be used for the evaluation of machine learning algorithms to determine their applicability in real-world scenarios. To address this, the Time-evolving Data Science and Artificial Intelligence for Advanced Open Environmental Science (TAIAO) project in New Zealand aims to provide a collection of datasets and accompanying example notebooks for their analysis. This paper showcases three New Zealand-based annotated image datasets that form part of the collection. The first dataset contains annotated images of various predator species, mainly small invasive mammals, taken using low-light camera traps predominantly at night. The second provides aerial photography of the Waikato region in New Zealand, in which stands of Kahikatea (a native New Zealand tree) have been marked up using manual segmentation. The third is a dataset containing orthorectified high-resolution aerial photography, paired with satellite imagery taken by Sentinel-2. Additionally, the TAIAO web platform also contains a collated list of other datasets provided and licensed by our data partners that may be of interest to other researchers.

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展示TAIAO项目:从新西兰自然环境图像中为机器学习提供资源
对地球的自然资源进行适当的管理是防止自然环境进一步退化的必要条件。然而,为知情的资源规划和保护所需的环境数据集收集和注释可能是昂贵的。因此,缺乏公开可用的数据集,特别是与环境保护相关的注释图像数据集,可用于评估机器学习算法,以确定其在现实世界场景中的适用性。为了解决这个问题,新西兰的先进开放环境科学(TAIAO)项目旨在提供一系列数据集和附带的示例笔记本,以供分析。本文展示了三个基于新西兰的注释图像数据集,它们构成了该集合的一部分。第一个数据集包含各种掠食者物种的注释图像,主要是小型入侵哺乳动物,主要是在夜间使用低光相机陷阱拍摄的。第二张图提供了新西兰怀卡托地区的航拍照片,其中Kahikatea(一种新西兰本土树木)的树木被人工分割。第三个是包含正校正高分辨率航空摄影的数据集,与Sentinel-2拍摄的卫星图像配对。此外,TAIAO网络平台还包含由我们的数据合作伙伴提供和许可的其他数据集的整理列表,这些数据集可能会引起其他研究人员的兴趣。
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来源期刊
Journal of the Royal Society of New Zealand
Journal of the Royal Society of New Zealand 综合性期刊-综合性期刊
CiteScore
4.60
自引率
0.00%
发文量
74
审稿时长
3 months
期刊介绍: Aims: The Journal of the Royal Society of New Zealand reflects the role of Royal Society Te Aparangi in fostering research and debate across natural sciences, social sciences, and the humanities in New Zealand/Aotearoa and the surrounding Pacific. Research published in Journal of the Royal Society of New Zealand advances scientific knowledge, informs government policy, public awareness and broader society, and is read by researchers worldwide.
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